Object detection has always been a challenging and exciting area of AI. From detecting small objects to improving accuracy in crowded scenes, every improvement can make a big difference. That’s why I’m excited to share my latest project: YOLO Slicing for segmentation.
The Ultralyics library is a powerful toolkit for building state-of-the-art AI models, including YOLO (You Only Look Once) for object detection. It simplifies training, inference, and deployment, making it accessible for both beginners and experts. Learn more about the library and its capabilities here.
This project is all about maximizing the power of YOLOv11s-seg for object detection, and I’ve built three scripts to demonstrate how slicing can dramatically improve accuracy. Let’s dive into what makes these scripts so effective and how they can help you take your object detection tasks to the next level.
The Challenge of Small Object Detection
Traditional object detection methods often struggle with small or partially visible objects, especially in busy environments. This is where slicing techniques come into play. By dividing a frame into smaller sections and processing each section independently, we can improve detection accuracy without sacrificing performance. Sounds cool, right?
That’s the concept behind the three scripts I’ve developed:
- Standard YOLO Segmentation (
yoloDetect.py) - 4-Slice Segmentation (
yoloSliceDetect.py) - 12-Slice Segmentation (
yoloSuperSliceDetect.py)
Each script builds upon the previous one to progressively enhance detection capabilities.
The Scripts in Action
1. yoloDetect.py
This is your starting point. The script processes the entire video frame as one unit, performing standard YOLO segmentation. It’s quick, efficient, and great for general use cases. However, if you’re working with smaller objects or complex scenes, you might need something more sophisticated.
2. yoloSliceDetect.py
This script steps up the game by dividing the frame into four overlapping slices (2 rows × 2 columns). With a 10% overlap, it ensures seamless detection across boundaries. This approach is especially useful for detecting smaller objects that might otherwise go unnoticed.
3. yoloSuperSliceDetect.py
Here’s where things get serious. By dividing the frame into 12 overlapping slices (4 rows × 3 columns), this script is optimized for handling complex scenes with a high density of small or partially visible objects. It strikes a balance between speed, quality, and accuracy, making it my go-to for challenging scenarios.
How You Can Use These Scripts
Getting started is easy. Here’s what you’ll need:
- Install the required libraries:
pip install ultralytics opencv-python numpy - Download the YOLO model file (
yolo11s-seg.pt) and place it in the same directory as the scripts. - Add your video file (e.g.,
input3.mp4) to the directory.
Run the scripts with these commands:
- For standard segmentation:
python yoloDetect.py - For 4-slice segmentation:
python yoloSliceDetect.py - For 12-slice segmentation:
python yoloSuperSliceDetect.py
Once the scripts process your video, you’ll get an annotated video file (instance-segmentation3.avi) showcasing the detection results.
Watch the Full Explanation
If you’re curious about how these scripts work or want to see them in action, check out my YouTube video. I’ve broken down the process and highlighted the key benefits of slicing techniques.
Ready to Level Up Your Detection Game?
These scripts are just the beginning. Whether you’re working on AI-powered surveillance, quality control, or any application requiring precise object detection, slicing can be a game-changer.
Get the Code
These scripts are hosted on github. Get them here: https://github.com/jsammarco/YoloSlicing
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